2003 LanguageIndependentAuthorshipAt

• (Peng et al., 2003) ⇒ Fuchun Peng, Dale Schuurmans, Shaojun Wang, and Vlado Keselj. (2003). “Language Independent Authorship Attribution Using Character Level Language Models.” In: Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1. ISBN:1-333-56789-0 doi:10.3115/1067807.1067843

Subject Headings: Character-level Language Model.

Cited By

• http://scholar.google.com/scholar?q=%222003%22+Language+Independent+Authorship+Attribution+Using+Character+Level+Language+Models
• http://dl.acm.org/citation.cfm?id=1067807.1067843&preflayout=flat#citedby

Quotes

Abstract

We present a method for computer-assisted authorship attribution based on character-level n-gram language models. Our approach is based on simple information theoretic principles, and achieves improved performance across a variety of languages without requiring extensive pre-processing or feature selection. To demonstrate the effectiveness and language independence of our approach, we present experimental results on Greek, English, and Chinese data. We show that our approach achieves state of the art performance in each of these cases. In particular, we obtain a 18% accuracy improvement over the best published results for a Greek data set, while using a far simpler technique than previous investigations.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 LanguageIndependentAuthorshipAt	Fuchun Peng Dale Schuurmans Shaojun Wang Vlado Keselj			Language Independent Authorship Attribution Using Character Level Language Models				10.3115/1067807.1067843		2003